| Item Type |
Monograph
(Working Paper)
|
| Abstract |
We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end we apply causal machine learning on the earnings announcements of a wide cross-section of US companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher. |
| Authors |
Audrino, Francesco; Chassot, Jonathan; Huang, Chen; Knaus, Michael; Lechner, Michael & Ortega Lahuerta, Juan-Pablo |
| Language |
English |
| Subjects |
economics
finance |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
SEPS - Quantitative Economic Methods |
| Date |
22 December 2020 |
| Number of Pages |
30 |
| Contact Email Address |
francesco.audrino@unisg.ch |
| Depositing User |
Prof. Ph.D Francesco Audrino
|
| Date Deposited |
22 Dec 2020 13:00 |
| Last Modified |
21 Nov 2022 14:27 |
| URI: |
https://www.alexandria.unisg.ch/publications/261799 |
How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning
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